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Bioinformatics Advance Access first published online on August 25, 2007
This version published online on September 1, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm412
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A comparison of background correction methods for two-colour microarrays

Matthew E. Ritchie a, Jeremy Silver b, Alicia Oshlack b, Melissa Holmes c, Dileepa Diyagama d, Andrew Holloway d and Gordon K. Smyth b,*

aDepartment of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom. bBioinformatics Division, cImmunology Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia, dThe Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, Victoria 3002, Australia

*To whom correspondence should be addressed. Gordon K. Smyth, E-mail: smyth{at}wehi.edu.au


  Abstract

Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments.

Results: Using data where some independent truth in gene expression is known, 8 different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates.Methods which stabilise the variances of the log-ratios along the intensity range perform the best.The normexp + offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp isapplicable to most types of two-colour microarray data.

Availability: The background correction methods compared in this paper are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. Supplementary Information is available from http://bioinf.wehi.edu.au/resources/webReferences.html.

Contact: smyth{at}wehi.edu.au

Associate Editor: Dr. Trey Ideker

Received on April 16, 2007; revised on July 20, 2007; accepted on August 9, 2007

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